Apparatuses, methods, and computer program products for safety compliance determinations

ABSTRACT

Apparatuses, methods, and computer program products for safety compliance determinations are provided. An example method includes receiving three-dimensional (3D) image data indicative of a field of view of a 3D imager that includes a first user upon which to perform a compliance determination. The method further includes generating a fit parameter associated with a safety device of the first user within the field of view of the 3D imager based upon the 3D image data, the fit parameter indicative of an associated positioning of the safety device relative to the first user. The method also includes comparing the fit parameter with a compliance threshold associated with the safety device and generating an alert signal in an instance in which the fit parameter fails to satisfy the compliance threshold. In some instances, the method may supply the 3D image data to an artificial neural network to generate the fit parameter.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 16/953,957, filed Nov. 20, 2020, now U.S. Pat. No.11,354,850, which application is hereby incorporated by reference in itsentirety.

TECHNOLOGICAL FIELD

Example embodiments of the present disclosure relate generally to safetysystems and, more particularly, to the detection of the noncompliant useof safety devices.

BACKGROUND

In many environments, such as manufacturing facilities, productionlines, and/or the like, workers (e.g., employees, contractors, staff,etc.) may be subject to various harmful conditions as part of performingtheir associated duties in these environments. Without the proper use ofsafety devices, often mandated by applicable industry regulations, theseconditions may injury these workers. For example, some industriesrequire that workers use ear plugs or other hearing protection to reduceor avoid ear damage associated with sufficiently loud work environments.The inventors have identified numerous deficiencies with these existingtechnologies in the field, the remedies for which are the subject of theembodiments described herein.

BRIEF SUMMARY

As noted above, many industries and environments are associated withvarious conditions that may be harmful to employees, contractors, staff,etc. that work in these environments. By way of example, some industrialenvironments may, as part of normal operation, produce sound that isdamaging to a worker's ears and/or produce dust, suspended particulates,caustic chemicals, flying objects, and/or the like that are potentiallydamaging to a worker's eyes. As such, many industry regulations requirethat workers use safety devices such as ear plugs, safetyglasses/goggles, or the like so as to reduce or eliminate the likelihoodof this damage. In order to provide the necessary protection from thesedangers, however, a user must properly fit (e.g., position, wear, etc.)the safety devices so that these devices may perform their intendedfunction. For example, in order for an ear plug to appropriately shieldan associated user's ears from harmful sound levels, the ear plugs mustbe properly fitted or positioned (e.g., at a sufficient depth in theuser's ear canal). Given that these safety devices are positioned by anassociated user, user error often results in a poor fit or improperpositioning of these devices.

Traditional systems that attempt to review the use of safety devices byusers have relied upon, in the case of ear plugs or related hearingprotection, acoustic attenuation determinations. For example, thesetraditional systems may require that a particular user position (e.g.,insert) a required safety device (e.g., hearing protection) and performdirect acoustical measurements in order to determine the noise reduction(e.g., attenuation) provided by the particular positioning of the safetydevice. Furthermore, traditional systems that attempt to review imagesof a user wearing safety devices to determine compliance rely upontwo-dimensional (2D) data that lacks the ability to recognize or analyzethe depth of items contained within the 2D data (e.g., lacks image datain a third dimension). As such, these systems may require a plurality of2D images from different positions, angles, etc. relative to the userand further analysis of these 2D images in order to ascertain depth(e.g., data in the third dimension). Accordingly, such conventionaltechniques are time consuming to perform resulting in inefficient safetydeterminations, especially in high traffic environments (e.g., having alarge number of workers subject to safety determinations).

To solve these issues and others, example implementations of embodimentsof the present disclosure may leverage three-dimensional (3D) image dataand machine learning techniques (e.g., artificial neural networks,convolutional neural networks, or the like) to, in near real-time,provide safety compliance determinations. In operation, embodiments ofthe present disclosure may generate fit parameters associated withsafety devices of a user captured within a field of view of a 3D imagerthat is indicative of an associated positioning of the safety device(s)relative to this user. Generation of this fit parameter may includesupplying 3D image data to an artificial neural network that comparesnumerical values (e.g., values associated with coordinates of verticesforming polygons within the field of view of the 3D imager) of thesupplied 3D image data with 3D image data of the artificial neuralnetwork or convolutional neural network (e.g., the artificial neuralnetwork or convolutional neural network is trained on 3D image data).Comparison between the fit parameter and associated compliancethresholds for the specific safety device may be used to quickly andreliable determine proper fit or positioning of a safety device withoutthe need for additional image data (e.g., a plurality of 2D imagecaptures) or further testing (e.g. attenuation testing).

Apparatuses, methods, systems, devices, and associated computer programproducts are provided for safety compliance determinations. An examplemethod for safety compliance determinations may include receivingthree-dimensional (3D) image data, the 3D image data indicative of afield of view of a 3D imager that includes a first user upon which toperform a compliance determination. The method may further includegenerating a fit parameter associated with a safety device of the firstuser within the field of view of the 3D imager based upon the 3D imagedata, wherein the fit parameter is indicative of an associatedpositioning of the safety device relative to the first user. The methodmay also include comparing the fit parameter with a compliance thresholdassociated with the safety device and generating an alert signal in aninstance in which the fit parameter fails to satisfy the compliancethreshold.

In some embodiments, generating the alert signal may further includegenerating an adjustment notification including a modification of thepositioning of the safety device relative to the first user.

In some embodiments, generating the alert signal may further includepreventing access for the first user to one or more systems.

In some embodiments, determining the fit parameter may further includesupplying the 3D image data to an artificial neural network.

In some embodiments, the 3D image data includes an N-dimensional matrixcontaining one or more values indicative of coordinates of verticesforming polygons within the field of view of the 3D imager including thefirst user. In such an embodiment, the method may further includereducing the N-dimensional matrix into a one-dimensional (1D) array anddetermining the fit parameter based upon a comparison between each valueof the 1D array and one or more values associated with 3D image dataindicative of the field of view of the 3D imager that includes a seconduser. In other embodiments, 3D image data that includes an N-dimensionalmatrix containing one or more values indicative of coordinates ofvertices forming polygons within the field of view of the 3D imager maybe supplied to a convolutional neural network that employs, for example,3D kernels.

In some further embodiments, the method may include modifying thecompliance threshold associated with the safety device based upon one ormore iterative determinations of the fit parameter associated with thesafety device.

The above summary is provided merely for purposes of summarizing someexample embodiments to provide a basic understanding of some aspects ofthe disclosure. Accordingly, it will be appreciated that theabove-described embodiments are merely examples and should not beconstrued to narrow the scope or spirit of the disclosure in any way. Itwill be appreciated that the scope of the disclosure encompasses manypotential embodiments in addition to those here summarized, some ofwhich will be further described below.

BRIEF DESCRIPTION OF THE DRAWINGS

Having described certain example embodiments of the present disclosurein general terms above, reference will now be made to the accompanyingdrawings. The components illustrated in the figures may or may not bepresent in certain embodiments described herein. Some embodiments mayinclude fewer (or more) components than those shown in the figures.

FIG. 1A illustrates an example safety compliance system and handheld 3Dsensor device in accordance with some example embodiments describedherein;

FIGS. 1B-1C illustrate example stationary 3D sensor devices for use withthe safety compliance system of FIG. 1A, in accordance with some exampleembodiments described herein;

FIG. 2 illustrates a schematic block diagram of example circuitry thatmay perform various operations, in accordance with some exampleembodiments described herein;

FIG. 3 illustrates an example flowchart for safety compliancedeterminations, in accordance with some example embodiments describedherein;

FIG. 4 illustrates an example flowchart for fit parameter generation, inaccordance with some example embodiments described herein; and

FIGS. 5A-5B illustrate example 3D image data associated with a poor fitand a proper fit, respectively, in accordance with some exampleembodiments described herein.

DETAILED DESCRIPTION

Some embodiments of the present disclosure will now be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the disclosure are shown. Indeed, thisdisclosure may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to like elements throughout. Asused herein, the description may refer to a computing device of anexample safety compliance system as an example “apparatus.” However,elements of the apparatus described herein may be equally applicable tothe claimed method and computer program product. Thus, use of any suchterms should not be taken to limit the spirit and scope of embodimentsof the present disclosure.

Definition of Terms

As used herein, the terms “data,” “content,” “information,” “electronicinformation,” “signal,” “command,” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, received,and/or stored in accordance with embodiments of the present disclosure.Thus, use of any such terms should not be taken to limit the spirit orscope of embodiments of the present disclosure. Further, where a firstcomputing device is described herein to receive data from a secondcomputing device, it will be appreciated that the data may be receiveddirectly from the second computing device or may be received indirectlyvia one or more intermediary computing devices, such as, for example,one or more servers, relays, routers, network access points, basestations, hosts, and/or the like, sometimes referred to herein as a“network.” Similarly, where a first computing device is described hereinas sending data to a second computing device, it will be appreciatedthat the data may be sent directly to the second computing device or maybe sent indirectly via one or more intermediary computing devices, suchas, for example, one or more servers, remote servers, cloud-basedservers (e.g., cloud utilities), relays, routers, network access points,base stations, hosts, and/or the like.

As used herein, the term “comprising” means including but not limited toand should be interpreted in the manner it is typically used in thepatent context. Use of broader terms such as comprises, includes, andhaving should be understood to provide support for narrower terms suchas consisting of, consisting essentially of, and comprised substantiallyof.

As used herein, the phrases “in one embodiment,” “according to oneembodiment,” “in some embodiments,” and the like generally refer to thefact that the particular feature, structure, or characteristic followingthe phrase may be included in at least one embodiment of the presentdisclosure. Thus, the particular feature, structure, or characteristicmay be included in more than one embodiment of the present disclosuresuch that these phrases do not necessarily refer to the same embodiment.

As used herein, the word “example” is used herein to mean “serving as anexample, instance, or illustration.” Any implementation described hereinas “example” is not necessarily to be construed as preferred oradvantageous over other implementations.

As used here, the terms “three-dimensional imager” and “3D imager” referto devices capable of generating 3D image data. Example 3D imagers mayinclude 3D cameras, stereo cameras, depth cameras, time-of-flight (TOF)cameras or sensors, range cameras, and/or the like that may generateimage data in three dimensions. Said differently, a 3D imager of thepresent disclosure may include any device configured to generate imagedata that includes an associated depth or is otherwise capable ofdetermining or resolving a distance between the 3D imager and thesubject for each point of the image captured by the 3D imager (e.g.,represented by 3D image data).

As used herein, the term “computing device” refers to any user device,controller, object, or system which may be in network communication witha 3D imager (e.g., mobile or stationary) as described hereafter. Forexample, the computing device may refer to a wireless electronic deviceconfigured to perform various fit parameter related operations inresponse to 3D image data generated by the 3D imager. The computingdevice may be configured to communicate with the 3D imager viaBluetooth, NFC, Wi-Fi, 3G, 4G, 5G protocols, and the like. In someinstances, the computing device may comprise the 3D imager (e.g., anintegrated configuration).

As used herein, the term “computer-readable medium” refers tonon-transitory storage hardware, non-transitory storage device ornon-transitory computer system memory that may be accessed by acomputing device, a microcomputing device, a computational system or amodule of a computational system to encode thereon computer-executableinstructions or software programs. A non-transitory “computer-readablemedium” may be accessed by a computational system or a module of acomputational system to retrieve and/or execute the computer-executableinstructions or software programs encoded on the medium. Exemplarynon-transitory computer-readable media may include, but are not limitedto, one or more types of hardware memory, non-transitory tangible media(for example, one or more magnetic storage disks, one or more opticaldisks, one or more USB flash drives), computer system memory or randomaccess memory (such as, DRAM, SRAM, EDO RAM), and the like.

Having set forth a series of definitions called-upon throughout thisapplication, an example system architecture and example apparatus isdescribed below for implementing example embodiments and features of thepresent disclosure.

Device Architecture and Example Apparatus

With reference to FIG. 1A, a safety compliance system 100 is illustratedwith a handheld 3D sensor device 101 operably coupled with a computingdevice 200 via a network 104. The handheld 3D sensor device 101 maydefine a housing 106 that supports a 3D imager 110. The housing 106 may,in some embodiments as shown in FIG. 1A, be formed so as to be movablerelative a first user (e.g., first user 116 in FIG. 1C). Althoughillustrated with a mobile, handheld housing 106, the present disclosurecontemplates that the safety compliance system 100 may include anysensor device having any associated shape or form factor as describedhereafter with reference to FIGS. 1B-1C. The handheld 3D sensor device101 may, in some embodiments, further include an actionable element 108(e.g., trigger mechanism or the like) configured to, in response to auser input, control operation of the 3D imager 110 in whole or in part.For example, the actionable element 108 may define a trigger mechanismor assembly that a user may actuate (e.g., compress the trigger) tocause the 3D imager 110 to generate 3D image data (e.g., cause the 3Dimager 110 to capture a 3D image of the FOV of the 3D imager 110).Although illustrated with a trigger mechanism as the actionable element108, the present disclosure contemplates that the actionable element 108may include any feature (e.g., slider, button, etc.) configured toreceive a user input.

As defined above, the 3D imager 110 may comprise a device capable ofgenerating 3D image data and may include, for example, one or more 3Dcameras, stereo cameras, depth cameras, time-of-flight (TOF) cameras orsensors, range cameras, and/or the like that may generate image data inthree dimensions. The 3D imager 110 as shown may generate 3D image data(e.g., image data that includes an associated depth) of a field of view(FOV) associated with the 3D imager 110. By way of example, inoperation, the handheld 3D sensor device 101 may be positioned proximatea first user such that the 3D imager 110 may generate 3D image data(e.g., capture a 3D image) of the first user within the FOV of the 3Dimager 110. By way of a particular example, the handheld sensor device101 may be positioned proximate the ear of a first user (e.g., firstuser 116 in FIG. 1C) such that the user's ear and an associated safetydevice (e.g., ear plug) are captured by the 3D imager 110 (e.g., as 3Dimage data).

With reference to FIGS. 1B-1C, stationary sensor devices 102, 103 areillustrated for use as an alternative to or in addition to the handheld3D sensor device 101 as part of the safety compliance system 100. Insome embodiments the safety compliance system 100 may be formed as partof a building access management system so as to ensure safety compliancebefore providing access for a particular user to one or more systemsassociated with the safety compliance system 100. By way of example,handheld sensor device 101 and/or stationary sensor devices 102, 103 maybe positioned at an entry or access point for a manufacturing facilityso as to, as described hereafter, confirm a proper fit for safetydevices before providing access to such a facility. As such, thestationary sensor device 102 may, for example, include a frame 114configured to support the 3D imager 110. The frame 114 (e.g., stand,tri-pod, etc.) may be configured so as to position the 3D imager 110proximate, for example, a first user's ear in order to generate 3D imagedata (e.g., captured 3D images) of the first user's ear and associatedsafety device (e.g., ear plug or other hearing protection). In someembodiments, as shown in FIG. 1C, the stationary sensor device 103 maybe configured to receive a first user 116 such that one or more 3Dimagers 110 may generate 3D image data that includes the first user 116.As shown, in some embodiments two (2) or more 3D imagers 110 may be usedby the safety compliance system 100 so as to generate, simultaneously orotherwise, 3D image data associated with a plurality of FOVs associatedwith each 3D imager 110. For example, stationary sensor device 103 maybe include two (2) 3D imagers 110 configured to simultaneously generate3D image data of a first user's ears.

Turning back to FIG. 1A, the safety compliance system 100 may include acomputing device 200 that is connected with one or more sensor devices(e.g., handheld sensor device 101 and/or stationary sensor devices 102,103) over a network 104. As described hereafter, in some instances, thehandheld sensor device 101 and/or stationary sensor devices 102, 103 maycomprise the computing device 200, in whole or in part. The computingdevice 200 may include circuitry, networked processors, or the likeconfigured to perform some or all of the apparatus-based (e.g., safetycompliance-based) processes described herein, and may be any suitableprocessing device and/or network server. In this regard, the computingdevice 200 may be embodied by any of a variety of devices. For example,the computing device 200 may be configured to receive/transmit data(e.g., 3D image data) and may include any of a variety of fixedterminals, such as a server, desktop, or kiosk, or it may comprise anyof a variety of mobile terminals, such as a portable digital assistant(PDA), mobile telephone, smartphone, laptop computer, tablet computer,or in some embodiments, a peripheral device that connects to one or morefixed or mobile terminals. Example embodiments contemplated herein mayhave various form factors and designs but will nevertheless include atleast the components illustrated in FIG. 2 and described in connectiontherewith.

In some embodiments, the computing device 200 may be located remotelyfrom the handheld sensor device 101, the stationary sensor device 102,and/or the stationary sensor device 103, although in other embodiments,the computing device 200 may comprise the handheld sensor device 101,the stationary sensor device 102, and/or the stationary sensor device103, in whole or in part. The computing device 200 may, in someembodiments, comprise several servers or computing devices performinginterconnected and/or distributed functions. Despite the manyarrangements contemplated herein, the computing device 200 is shown anddescribed herein as a single computing device to avoid unnecessarilyovercomplicating the disclosure.

The network 104 may include one or more wired and/or wirelesscommunication networks including, for example, a wired or wireless localarea network (LAN), personal area network (PAN), metropolitan areanetwork (MAN), wide area network (WAN), or the like, as well as anyhardware, software and/or firmware for implementing the one or morenetworks (e.g., network routers, switches, hubs, etc.). For example, thenetwork 104 may include a cellular telephone, mobile broadband, longterm evolution (LTE), GSM/EDGE, UMTS/HSPA, IEEE 802.11, IEEE 802.16,IEEE 802.20, Wi-Fi, dial-up, and/or WiMAX network. Furthermore, thenetwork 104 may include a public network, such as the Internet, aprivate network, such as an intranet, or combinations thereof, and mayutilize a variety of networking protocols now available or laterdeveloped including, but not limited to TCP/IP based networkingprotocols. Although illustrated in FIG. 1A with a network 104, thepresent disclosure contemplates that, in some embodiments, the computingdevice 200 may be formed as part of an example sensor device.

As illustrated in FIG. 2 , the computing device 200 may include aprocessor 202, a memory 204, input/output circuitry 206, andcommunications circuitry 208. Moreover, the computing device 200 mayinclude image processing circuitry 210 and/or machine learning circuitry212. The computing device 200 may be configured to execute theoperations described below in connection with FIGS. 3-4 . Althoughcomponents 202-212 are described in some cases using functionallanguage, it should be understood that the particular implementationsnecessarily include the use of particular hardware. It should also beunderstood that certain of these components 202-212 may include similaror common hardware. For example, two sets of circuitry may both leverageuse of the same processor 202, memory 204, communications circuitry 208,or the like to perform their associated functions, such that duplicatehardware is not required for each set of circuitry. The use of the term“circuitry” as used herein includes particular hardware configured toperform the functions associated with respective circuitry describedherein. As described in the example above, in some embodiments, variouselements or components of the circuitry of the computing device 200 maybe housed within the handheld sensor device 101 and/or the stationarysensor devices 102, 103. It will be understood in this regard that someof the components described in connection with the computing device 200may be housed within one or more of the devices of FIGS. 1A-1C, whileother components are housed within another of these devices, or by yetanother device not expressly illustrated in FIGS. 1A-1C.

Of course, while the term “circuitry” should be understood broadly toinclude hardware, in some embodiments, the term “circuitry” may alsoinclude software for configuring the hardware. For example, although“circuitry” may include processing circuitry, storage media, networkinterfaces, input/output devices, and the like, other elements of thecomputing device 200 may provide or supplement the functionality ofparticular circuitry.

In some embodiments, the processor 202 (and/or co-processor or any otherprocessing circuitry assisting or otherwise associated with theprocessor) may be in communication with the memory 204 via a bus forpassing information among components of the computing device 200. Thememory 204 may be non-transitory and may include, for example, one ormore volatile and/or non-volatile memories. In other words, for example,the memory may be an electronic storage device (e.g., a non-transitorycomputer readable storage medium). The memory 204 may be configured tostore information, data, content, applications, instructions, or thelike, for enabling the computing device 200 to carry out variousfunctions in accordance with example embodiments of the presentdisclosure.

The processor 202 may be embodied in a number of different ways and may,for example, include one or more processing devices configured toperform independently. Additionally or alternatively, the processor mayinclude one or more processors configured in tandem via a bus to enableindependent execution of instructions, pipelining, and/ormultithreading. The use of the term “processing circuitry” may beunderstood to include a single core processor, a multi-core processor,multiple processors internal to the computing device, and/or remote or“cloud” processors.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory 204 or otherwise accessible to theprocessor 202. Alternatively or additionally, the processor 202 may beconfigured to execute hard-coded functionality. As such, whetherconfigured by hardware or by a combination of hardware with software,the processor 202 may represent an entity (e.g., physically embodied incircuitry) capable of performing operations according to an embodimentof the present disclosure while configured accordingly. Alternatively,as another example, when the processor 202 is embodied as an executor ofsoftware instructions, the instructions may specifically configure theprocessor 202 to perform the algorithms and/or operations describedherein when the instructions are executed.

The computing device 200 further includes input/output circuitry 206that may, in turn, be in communication with processor 202 to provideoutput to a user and to receive input from a user, user device, oranother source. In this regard, the input/output circuitry 206 maycomprise a display that may be manipulated by a mobile application. Insome embodiments, the input/output circuitry 206 may also includeadditional functionality including a keyboard, a mouse, a joystick, atouch screen, touch areas, soft keys, a microphone, a speaker, or otherinput/output mechanisms. The processor 202 and/or user interfacecircuitry comprising the processor 202 may be configured to control oneor more functions of a display through computer program instructions(e.g., software and/or firmware) stored on a memory accessible to theprocessor (e.g., memory 204, and/or the like).

The communications circuitry 208 may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data from/to anetwork and/or any other device, circuitry, or module in communicationwith the computing device 200. In this regard, the communicationscircuitry 208 may include, for example, a network interface for enablingcommunications with a wired or wireless communication network. Forexample, the communications circuitry 208 may include one or morenetwork interface cards, antennae, buses, switches, routers, modems, andsupporting hardware and/or software, or any other device suitable forenabling communications via a network. Additionally or alternatively,the communication interface may include the circuitry for interactingwith the antenna(s) to cause transmission of signals via the antenna(s)or to handle receipt of signals received via the antenna(s). Thesesignals may be transmitted by the computing device 200 using any of anumber of wireless personal area network (PAN) technologies, such asBluetooth® v1.0 through v3.0, Bluetooth Low Energy (BLE), infraredwireless (e.g., IrDA), ultra-wideband (UWB), induction wirelesstransmission, or the like. In addition, it should be understood thatthese signals may be transmitted using Wi-Fi, Near Field Communications(NFC), Worldwide Interoperability for Microwave Access (WiMAX) or otherproximity-based communications protocols.

Image processing circuitry 210 includes hardware components designed togenerate a fit parameter associated with a safety device of the firstuser within the field of view of the 3D imager 110 based upon the 3Dimage data. Image processing circuitry 210 may utilize processingcircuitry, such as the processor 202, to perform its correspondingoperations, and may utilize memory 204 to store collected information.In some instances, the image processing circuitry 210 may furtherinclude machine learning circuitry 212 that includes hardware componentsdesigned to analyze an N-dimensional matrix containing one or morevalues indicative of coordinates of vertices forming polygons within thefield of view of the 3D imager including the first user to generate thefit parameter. By way of example, machine learning circuitry 212 maycomprise or leverage an artificial neural network or convolutionalneural network trained on at least 3D image data of a second user (e.g.,a plurality of other users). The machine learning circuitry 212 may alsoutilize processing circuitry, such as the processor 202, to perform itscorresponding operations, and may utilize memory 204 to store collectedinformation.

It should also be appreciated that, in some embodiments, the imageprocessing circuitry 210 and/or the machine learning circuitry 212 mayinclude a separate processor, specially configured field programmablegate array (FPGA), or application specific interface circuit (ASIC) toperform its corresponding functions.

In addition, computer program instructions and/or other type of code maybe loaded onto a computer, processor or other programmable circuitry toproduce a machine, such that the computer, processor other programmablecircuitry that execute the code on the machine create the means forimplementing the various functions, including those described inconnection with the components of computing device 200.

As described above and as will be appreciated based on this disclosure,embodiments of the present disclosure may be configured as apparatuses,systems, methods, and the like. Accordingly, embodiments may comprisevarious means including entirely of hardware or any combination ofsoftware with hardware. Furthermore, embodiments may take the form of acomputer program product comprising instructions stored on at least onenon-transitory computer-readable storage medium (e.g., computer softwarestored on a hardware device). Any suitable computer-readable storagemedium may be utilized including non-transitory hard disks, CD-ROMs,flash memory, optical storage devices, or magnetic storage devices.

Example Operations for Safety Compliance Determinations

FIG. 3 illustrates a flowchart containing a series of operations forsafety compliance determinations. The operations illustrated in FIG. 3may, for example, be performed by, with the assistance of, and/or underthe control of an apparatus (e.g., computing device 200), as describedabove. In this regard, performance of the operations may invoke one ormore of processor 202, memory 204, input/output circuitry 206,communications circuitry 208, image processing circuitry 210, and/ormachine learning circuitry 212.

As shown in operation 305, the apparatus (e.g., computing device 200)includes means, such as processor 202, communications circuitry 208,image processing circuitry 210, or the like, for receivingthree-dimensional (3D) image data indicative of a field of view (FOV) ofa 3D imager 110 that includes a first user upon which to perform acompliance determination. As described above, the 3D imager 110 may beconfigured to capture the FOV of the 3D imager 110 as 3D image data thatis a 3D representation (e.g., including a depth determination) of thisFOV. By way of example, the first user may be positioned proximate the3D imager 110 (e.g., stationary sensor devices 102, 103) and/or the 3Dimager 110 may be positioned proximate the first user (e.g., handheldsensor device 101). In response to an instruction from an operator ofthe safety compliance system 100, via the actionable element 108, via anelectronic communication from the computing device 200, or the like, the3D imager 110 may generate 3D image data that captures a 3D image of thefirst user within the FOV of the 3D imager 110 (e.g., as 3D image data).

The 3D image data generated by the 3D imager 110 may, as describedhereafter, include numerical values representative of the 3D coordinatesof the vertices forming polygons within the field of view of the 3Dimager 310. For example, the 3D image data generated by the 3D imager110 may include numerical values of coordinates associated with therelative position of a particular vertex (e.g., x and y coordinates)within the FOV of the 3D imager 110. Due to the 3D nature of the 3Dimager 110, however, the 3D image data may also include numerical valuesof coordinates associated with the relative distance (e.g., depth or zcoordinate) between the 3D imager 110 and the subject (e.g., the objectswithin the FOV of the 3D imager 110). Each vertex within the field ofview of the 3D imager 110 may include a plurality of said numericalvalues that may, in some embodiments, be contained within anN-dimensional matrix.

By way of a particular example, the 3D image data generated by the 3Dimager 110 may be stored in a polygon file format (PLY) that describesan object as a collection of vertices, faces, and the like along withvarious properties (e.g., color, normal direction, etc.) attached tothese elements. The 3D image data, stored as a PLY file, may contain thedescription of hand-digitized objects, polygon objects from modelingprograms, range data, triangles from marching cubes (e.g., iso-surfacesfrom volume data), terrain data, radiosity models, and/or the like.Additionally, example properties that might be generated as 3D imagedata by the 3D imager 110 and stored with an example object as a PLYfile may include color, surface normals, texture coordinates,transparency, range data confidence, and/or other properties for thefront and/or the back of a polygon. As described herein, 3D image data(e.g., a PLY object) may include a list or N-dimensional matrix of x, y,and z coordinates for vertices and faces that are described by indicesinto the list or matrix of vertices. Said differently, vertices andfaces are example elements, and the PLY file operates as a list ofelements. Each element in a given PLY file may include a fixed number ofproperties as described above that are specified for each element.

In embodiments in which the computing device 200 and the 3D imager 110are contained with a common device or integrated device (e.g., thecomputing device 200 comprises the 3D imager 110), the 3D image data maybe received by the computing device 200 as described above. In otherembodiments in which the computing device 200 is located separated fromthe 3D imager 110, such as connected via network 104, the computingdevice 200 may be configured to receive the 3D image data from the 3Dimager 110 in response to generation of the 3D image data. Saiddifferently, each instance of 3D image data generation may betransmitted to the computing device 200 upon generation. In otherembodiments, the computing device 200 may periodically (e.g., accordingto a defined rate) request 3D image data from the 3D imager 110. In someembodiments, the 3D image data may be generated by the 3D imager 110and/or transmitted to the computing device 200 in response to an actionby the first user within the FOV of the 3D imager. By way of example, afirst user (e.g., first user 116 in FIG. 1C) may attempt to enter amanufacturing facility that employs one or more features of the safetycompliance system 100. The attempt to access such a facility (e.g.,scanning of an identification badge, attempt to open an door, attempt topass an access point, or the like) may cause the 3D imager 110 tocapture a 3D image (e.g., generate 3D image data) that includes thefirst user within the FOV of the 3D imager. Furthermore, in someembodiments, the 3D imager 110 may continuously generate 3D image data,and, in response to an access attempt by the first user, the computingdevice 200 may transmit a request to the 3D imager 110 data for 3D imagedata that includes the first user.

As shown in operation 310, the apparatus (e.g., computing device 200)includes means, such as processor 202, image processing circuitry 210,machine learning circuitry 212 or the like, for generating a fitparameter associated with a safety device of the first user within thefield of view of the 3D imager 110 based upon the 3D image data. Asdescribed hereafter, the fit parameter may be indicative of or basedupon an associated positioning of the safety device relative to thefirst user. As described above, in some embodiments, a user may berequired to wear a safety device that is designed for hearingprotection, such as an ear plug that is inserted into the ear canal ofthe user, in order to work in loud environments. As such, the 3D imagedata generated at operation 305 that includes a first user may furtherinclude 3D image data (e.g., a captured 3D image) of a safety device(e.g., an ear plug) positioned relative to the first user. Saiddifferently, the 3D image data may include numerical values associatedwith the coordinates of the vertices of polygons associated with the earplug (e.g., hearing protection).

The fit parameter may be generated based upon this 3D image data and maybe based upon the associated positioning of the safety device relativeto the first user. By way of continued example, the fit parameter may beindicative of or based upon a relative insertion depth of the ear plugwithin the first user's ear canal. Said differently, a properly fittedor positioned ear plug may be sufficiently inserted (e.g., as comparedto an associated threshold described hereafter) into the ear canal toreduce or prevent sound waves from entering the first user's ear. Apoorly fitted or positioned ear plug may not be sufficiently inserted(e.g., as compared to an associated threshold described hereafter) intothe ear canal to reduce or prevent sound waves from entering the firstuser's ear. As described hereafter with reference to FIG. 4 ,determination of the fit parameter may include supplying the 3D imagedata to an artificial neural network, convolutional neural network, orother machine learning system in order to analyze the 3D image data toidentify the relative positioning of the safety device (e.g., ear plug)and output an associated confidence value (e.g., fit parameter).Although described herein with reference to an example artificial neuralnetwork, the present disclosure contemplates that any image processing,computer vision, and/or machine learning technique may be used basedupon the intended application of the 3D imager 110 and safety compliancesystem 100.

As described above, the 3D image data may include an N-dimensionalmatrix containing numerical values indicative of coordinates of verticesforming polygons within the field of view of the 3D imager 110 includingthe first user. The artificial neural network utilized by the, forexample, machine learning circuitry 212 may be trained upon a pluralityof 3D image data generated by the 3D imager 110 (e.g., captured 3Dimages) that includes at least a second user. Although described hereinwith reference to a second user, the present disclosure contemplatesthat an example artificial neural network used by the safety compliancesystem 100 may be iteratively trained upon 3D image data that includes aplurality of users and associated safety devices at varying positionsrelative to the respective users. Said differently, such an artificialneural network may be trained upon sufficient 3D image data so as toascertain the position of the first user's ear and the position of thesafety device (e.g., ear plug) relative to the first user's ear (e.g.,an inserted depth of ear plug within the first user's ear canal). By wayof a particular example, the fit parameter may, in some embodiments,refer to a confidence of the computing device 200 (e.g., a confidence ofthe artificial neural network or convolutional neural network) that asafety device is properly positioned and may be based, at least in part,on a numerical distance (e.g., insertion distance) or numerical ratio ofthe inserted length of the ear plug relative to the total length of theear plug. By way of example, the system may be 50% confident that theear plug is properly positioned in a user's ear resulting in a fitparameter of 0.5 or 50%.

Thereafter, as shown in operation 315, the apparatus (e.g., computingdevice 200) includes means, such as processor 202, image processingcircuitry 210, machine learning circuitry 212, or the like, forcomparing the fit parameter with a compliance threshold associated withthe safety device. In order to define the appropriate positioning of thesafety device relative the first user, the computing device 200 mayemploy various compliance thresholds associated with respective safetydevices. By way of example, a vision-related safety device (e.g.,goggles, safety glasses, etc.) may be based upon or otherwise indicativeof an associated compliance threshold relating to the positioning of thesafety device relative the user's eye (e.g., a position thatsufficiently shields the user's eyes). Similarly, a hearing-relatedsafety device (e.g., ear plugs or the like) may be based upon orotherwise indicative of an associated compliance threshold related tothe positioning of the ear plug relative the user's ear (e.g., asufficient insertion distance so as to shield the user's ears). In someembodiments as described hereafter, each safety device may also includedevices of varying sizes, shapes, type, etc. For example, ear plugs mayvary in length, shape, cross-sectional area, material, and/or the like.As such, the present disclosure contemplates that the compliancethresholds and fit parameters described herein may be further configuredfor a safety device of a particular size, shape, type, etc. Thecompliance thresholds described herein may, in some embodiments, be setby applicable industry standards or regulations, set by a systemadministrator or set up procedure, or determined based upon iterativeanalysis of 3D image data by the artificial neural network.

With continued reference to operation 315, the compliance thresholdassociated with a hearing-related safety device such as an ear plug may,for example, define a minimum confidence value of 0.75 or 75%. In suchan example, the fit parameter generated at operation 310 may be comparedwith the compliance threshold to determine if the fit parametersatisfies the compliance threshold. For example, if the fit parametergenerated at operation 310 that is indicative of the system's confidencethat the ear plug is properly inserted into a user's ear exceeds 90%,then the fit parameter satisfies the compliance threshold. In such anembodiment, the safety compliance system 100 may determine that thepositioning of the safety device of the first user is satisfactory toreduce or prevent hearing damage and may, in some embodiments asdescribed herein, allow access for the first user to one or more systems(e.g., access to a manufacturing facility or the like). Althoughdescribed herein with reference to a compliance threshold of 0.75 or75%, the present disclosure contemplates that the compliance thresholdmay define any associated confidence value or parameter based upon theintended application of the safety compliance system 100.

In an instance in which the fit parameter fails to satisfy thecompliance threshold, as shown in operation 320, the apparatus (e.g.,computing device 200) includes means, such as processor 202,communications circuitry 208, input/output circuitry 206, or the like,for generating an alert signal. The alert signal may be indicative ofnoncompliance of the first user with regard to the positioning of thesafety device. In some embodiments, the alert signal may be displayed,for example by the input/output circuitry 206, for viewing by anoperator, administrator, or other user of the safety compliance system100. In some embodiments, the alert signal may be transmitted, forexample by the communications circuitry 208, to a user device associatedwith the first user. In such an embodiment, the alert signal may operateto notify the user of potential safety concerns associated with thepositioning of the first user's safety device(s).

In some embodiments, as shown in operation 325, the apparatus (e.g.,computing device 200) includes means, such as processor 202,communications circuitry 208, or the like, for generating an adjustmentnotification comprising a modification of the positioning of the safetydevice relative to the first user. In such an embodiment, the alertsignal generated at operation 320 may further include an adjustmentnotification for correcting the positioning issue associated with thesafety device of the first user. By way of example, the fit parametergenerated at operation 310 may fail to satisfy the compliance thresholdat operation 315 in that the fit parameter is indicative of a failure ofthe safety device (e.g., ear plug) to be properly positioned (e.g.,sufficiently inserted) relative the first user. As such, the adjustmentnotification generated at operation 325 may request a modification bythe first user of the positioning of said safety device so as toincrease the fit parameter (e.g., confidence value) such that the fitparameter satisfies the compliance threshold. By way of a particularexample, in an instance in which the fit parameter is 0.50 or 50% andthe compliance parameter is 0.60 or 60%, the adjustment notification mayrequest a modification that directs the first user to further insert,for example, the exposed ear plug so as to modify the fit parameter insatisfaction of the example compliance parameter.

In some embodiments, as shown in operation 330, the apparatus (e.g.,computing device 200) includes means, such as processor 202,communications circuitry 208, or the like, for preventing access for thefirst user to one or more systems. As described above, the computingdevice 200, 3D imager 110, or the like may be formed as part of abuilding access management system so as to ensure safety compliancebefore providing access for a particular user to one or more systemsassociated with these devices. By way of continued example, one or moredevices of the safety compliance system 100 may be positioned at anentry or access point for a manufacturing facility so as to confirm aproper fit for safety devices before providing access to such afacility. As such, in an instance in which the fit parameter fails tosatisfy the compliance threshold, the alert signal generated atoperation 320 may further include instructions to one or more systems(e.g., access gate, door, turnstile, or the like) that prevents access(e.g., physical access, electronic access, etc.) for the first user tothese systems. Said differently, the computing device 200 may beconfigured to, as described above, determine an improper or poor fit fora safety device (e.g., improper positioning of the safety devicerelative to the first user) such that the safety device fails toadequately protect the first user and may prevent the first user fromaccessing a location, system, etc. that may be harmful to the first useror otherwise requires proper safety device positioning. Althoughdescribed herein with reference to system access, the present disclosurecontemplates that the computing device 200 may modify any systemparameter, feature, element (e.g., physical or electronic) in responseto the determinations regarding safety compliance described herein.

FIG. 4 illustrates a flowchart containing a series of operations for fitparameter generation. The operations illustrated in FIG. 4 may, forexample, be performed by, with the assistance of, and/or under thecontrol of an apparatus (e.g., computing device 200), as describedabove. In this regard, performance of the operations may invoke one ormore of processor 202, memory 204, input/output circuitry 206,communications circuitry 208, image processing circuitry 210, and/ormachine learning circuitry 212.

As shown in operation 405, the apparatus (e.g., computing device 200)includes means, such as processor 202, communications circuitry 208,image processing circuitry 210, or the like, for receiving 3D image datacomprising an N-dimensional matrix containing one or more valuesindicative of coordinates of vertices forming polygons within the fieldof view of the 3D imager 110 including the first user. As describedabove, the 3D image data generated by the 3D imager 110 may includenumerical values representative of the 3D coordinates of the verticesforming polygons within the field of view of the 3D imager 110. Forexample, the 3D image data generated by the 3D imager 110 may includenumerical values of coordinates associated with the relative position ofa particular vertex (e.g., x and y coordinates) within the FOV of the 3Dimager 110. Due to the 3D nature of the 3D imager 110, however, the 3Dimage data may also include numerical values of coordinates associatedwith the relative distance (e.g., depth or z coordinate) between the 3Dimager 110 and the subject (e.g., the objects within the FOV of the 3Dimager 110). As shown below, example numerical values for the verticesforming polygons within the FOV of the 3D imager may include, forexample, coordinates associated with a first vertex (e.g., X₁, Y₁, Z₁),coordinates associated with a second vertex (e.g., X₂, Y₂, Z₂),coordinates associated with a third vertex (e.g., X₃, Y₃, Z₃), . . . ,coordinates associated with N number of vertices (e.g., X_(N), Y_(N),Z_(N)).

$\begin{bmatrix}X_{1} & Y_{1} & Z_{1} \\X_{2} & Y_{2} & Z_{2} \\X_{3} & Y_{3} & Z_{3} \\ \vdots & \vdots & \vdots \\X_{N} & Y_{N} & Z_{N}\end{bmatrix}$

As shown in operation 410, the apparatus (e.g., computing device 200)includes means, such as processor 202, image processing circuitry 210,machine learning circuitry 212, or the like, for reducing theN-dimensional matrix into a one-dimensional (1D) array. Given the volumeof polygons and associated vertices of the 3D image data, the number ofnumerical values (e.g., coordinates) of the N-dimensional matrix ofnumerical values may be numerous in volume. In order to supply thenumerical values of the 3D image data to an artificial neural network asdescribed above and hereafter, the computing device 200 may reduce,compress, flatten, etc. the N-dimensional matrix of numerical values toa one-dimensional (1D) array that comprises the numerical values. Asshown below, for example, coordinates associated with a first vertex(e.g., X₁, Y₁, Z₁), coordinates associated with a second vertex (e.g.,X₂, Y₂, Z₂), coordinates associated with a third vertex (e.g., X₃, Y₃,Z₃), . . . , coordinates associated with N number of vertices (e.g.,X_(N), Y_(N), Z_(N)) may be sequentially arranged in a 1D array or list.

$\begin{bmatrix}X_{1} & Y_{1} & {Z_{1}\ } & {X_{2}\text{  }} & Y_{2} & Z_{2} & \ldots & X_{N} & Y_{N} & Z_{N}\end{bmatrix}$

In some embodiments, as shown in operation 415, the apparatus (e.g.,computing device 200) includes means, such as processor 202, imageprocessing circuitry 210, machine learning circuitry 212, or the like,for supplying the 3D image data to an artificial neural network. Asdescribed above, an artificial neural network utilized by the machinelearning circuitry 212 may be trained upon a plurality of 3D image datagenerated by the 3D imager 110 (e.g., captured 3D images) that includesat least a second user (e.g., 3D images of a user other than the firstuser). In some instances, the artificial neural network may be suppliedwith a plurality of 3D image data of various captured 3D images of usersand associated safety devices. The artificial neural network may beiteratively trained upon this plurality of 3D image data in order todetermine commonalties, correlations, patterns, etc. between 3D imagedata so as to infer or otherwise determine the relative position ofobjects within the FOV of the 3D imager 110. By way of continuedexample, each instance of 3D image data generated by the 3D imager(e.g., each captured 3D image) may include an associated plurality ofcoordinates (e.g., numerical values for the x, y, and z positions ofvertices forming polygons within the field of view of the 3D imager).Each of these numerical values may be captured in an N-dimensionalmatrix that is further reduced to a 1D array as described above withreference to operations 405 and 410. Although described herein withrespect to an example second user, the present disclosure contemplatesthat the artificial neural network may be supplied with 3D image dataassociated with numerous users with varying types, shapes,configurations, positions, etc. of safety devices to further improve fitdeterminations by the artificial neural network.

During training of such an example artificial neural network iterativelysupplied with a plurality of 3D image data, the artificial neuralnetwork may be configured to, over time, determined patterns amongst theplurality of numerical values contained within various 1D arrays. Saiddifferently, the artificial neural network may be configured todetermine a correlation or pattern associated with the numerical valuesat particular locations within the 1D array so as to determineassociated locations of the user captured by the 3D image data. By wayof a particular example, the artificial neural network may analyzenumerous 1D arrays of 3D image data and identify that, for example, thenumerical values at location Z₃ are within a range of numerical valuesin each 1D array. Such a determination or correlation may occur for eachlocation within the array and may further include determinedrelationships between numerical values at different locations within thearray (e.g., a ratio between adjacent or nearby values or the like). Indoing so, the artificial neural network may process the 3D image dataand determine positions within the 3D image data that are associatedwith particular locations of the user within the FOV of the 3D imager110.

By way of a further example, the artificial neural network may determinethe location of an ear of a user by determining a pattern of numericalvalues that, within a set range or boundary, are relatively unchanged(or substantially similar) amongst different instances of 3D image data.Said differently, the artificial neural network may determine thatnumerical values associated with a user's ear are relatively the sameamongst different users (e.g., the positioning user's ear features areunchanged) but that numerical values associated with a safety device maybe different amongst different users (e.g., depending upon inserteddistance of the ear plug for example). As such, the artificial neuralnetwork may, for example, iteratively analyze numerical valuesassociated with positions within the 1D array that are different amongstinstances of 3D image data so as to determine numerical valuesassociated with various relative positions of the safety device (e.g.,ear plug) and the associated user (e.g., an inserted length ordistance). In some instances, the 3D image data supplied to theartificial neural network may be augmented in that the 3D image capturedby the 3D imager 110 may be, for example, rotated, flipped, etc. inorder to further improve fit parameter generation.

In some example artificial neural networks employed by the safetycompliance system 100 and computing device 200, the network may includea plurality of multiple, dense, fully connected layers (e.g., eachneuron is connected to all neurons from the previous layer as well asthe subsequent layer) each of which may include multiple units and mayinclude drop out layers. A rectified linear unit (ReLU) may be used asan activation function for each neuron in the artificial neural network.A last layer (e.g., another dense layer) with one (1) neuron and sigmoidactivation function may also be used in that a sigmoid function mayoperate with data that may be classified into two (2) classes (e.g., apoor fit or proper fit as illustrated in FIGS. 5A-5B). Furthermore, thesigmoid function may be associated with a confidence parameter (e.g., ashift in the decision line) so as to ensure that only classificationsare made that exceed the determined confidence. The confidence parametermay be set by a system administrator, applicable industry regulation, orthe like and may, for example, be set as 80% (e.g., a shift in thesigmoid function by 0.80) in some embodiments.

Although described herein with reference to an artificial neural networkat operation 415, the present disclosure contemplates that, in someembodiments, a convolutional neural network may be used. In such anexample embodiment, the convolutional neural network may be configuredto be trained on or otherwise process multidimensional inputs. Saiddifferently, an example convolutional neural network may receive 3Dimage data at operation 405 and not require the reduction of theN-dimensional matrix into a one-dimensional array at operation 410. Asdescribed above, the convolutional neural network may include 3D kernelsused for image processing to generate the fit parameter as describedherein.

Thereafter, as shown in operation 420, the apparatus (e.g., computingdevice 200) includes means, such as processor 202, image processingcircuitry 210, machine learning circuitry 212, or the like, fordetermining the fit parameter based upon a comparison between each valueof the 1D array and one or more values associated with 3D image dataindicative of the field of view of the 3D imager that includes a seconduser. As described above, the artificial neural network may be, forexample, trained upon a plurality of 3D image data of other users (e.g.,at least a second user). As such, the computing device 200 may beconfigured to determined numerical values (e.g., a range of numericalvalues, ratios between values, etc.) at particular positions within a 1Darray that are associated with relative positions between the user(e.g., a user's ear) and the safety device (e.g., ear plug). Atoperation 420, the computing device 200 may compare each value of the 1Darray (e.g. the 3D image data of the 3D image upon which to perform thecompliance determination) with 3D image data (e.g., numerical values,value ranges, etc.) of the artificial neural network to identifypatterns or correlations between these numerical values. By way ofexample, the comparison at operation 420 may indicate a similaritybetween numerical values of the 1D array and 3D image data associatedwith at least a second user (e.g., or a plurality of other users) thatis indicative of a safety device (e.g., ear plug) that is improperlyinserted into the user's ear canal. This comparison at operation 420 mayiteratively occur between the 3D image data of the first user and eachother instance of 3D image data or may occur between the 3D image dataof the first user and a composite (e.g., a combined numericalrepresentation) of a plurality of instances of 3D image data analyzed bythe artificial neural network. Following such comparisons, the computingdevice may generate a fit parameter that is indicative of an associatedpositioning of the safety device relative to the first user andexpressed as a value of the computing device's confidence in thisdetermination (e.g., how confidence the system is that the safety deviceis properly positioned).

In some embodiments, as shown in operation 425, the apparatus (e.g.,computing device 200) includes means, such as processor 202, imageprocessing circuitry 210, machine learning circuitry 212, or the like,for modifying the compliance threshold associated with the safety devicebased upon one or more iterative determinations of the fit parameterassociated with the safety device. By way of example, the iterativeperformance of the operations described herein in generation of a fitparameter including iterative analysis of 3D image data by an exampleartificial neural network may be used to improve subsequent safetycompliance determinations. For example, iterative fit parametergeneration and determinations may indicate, via an excess ofnoncompliant, poorly fitted, or improperly positioned safety devices,that the compliance threshold for a particular safety device is too highand should be modified (e.g., reduced). Similarly, iterative fitparameter generation and determinations may indicate, via an absence ofnoncompliant, poorly fitted, or improperly positioned safety devices,that the compliance threshold for a particular safety device is too lowand should be modified (e.g., increased). In such an embodiment, thecompliance threshold may also be iteratively modified during fitparameter generation in order to ensure accurate safety compliancedeterminations.

With reference to FIGS. 5A and 5B, example visual representations of 3Dimage data are illustrated. As shown in FIG. 5A, a noncompliant, poorlyfitted, or improperly positioned safety device (e.g., ear plug) 505 isillustrated in which the safety device is not sufficiently inserted intothe user's ear canal. In contrast, as shown in FIG. 5B, a compliant,properly fitted, and positioned safety device (e.g., ear plug) 510 isillustrated in which the safety device is sufficiently inserted into theuser's ear canal. In some embodiments, such visual representations maybe displayed to an operator or other user of the safety compliancesystem 100. In some embodiments, these visual representations may becolor-coded to indicate a relative depth (e.g., z dimensions of the 3Dimage data) to an operator and may operate as further opportunities forvisual inspection to ensure proper safety compliance. In doing so, themethods, systems, apparatuses, devices, and computer program products ofthe present disclosure may improve safety compliance determinationswithout the need for acoustic attenuation determinations and additional2D image data captures as required by traditional systems. In doing so,embodiments of the present disclosure eliminate the time consumingoperations of traditional systems by providing a solution that performsfit parameter generation and safety compliance determinations in nearreal-time.

FIGS. 3-4 thus illustrate flowcharts describing the operation ofapparatuses, methods, and computer program products according to exampleembodiments contemplated herein. It will be understood that eachflowchart block, and combinations of flowchart blocks, may beimplemented by various means, such as hardware, firmware, processor,circuitry, and/or other devices associated with execution of softwareincluding one or more computer program instructions. For example, one ormore of the operations described above may be implemented by anapparatus executing computer program instructions. In this regard, thecomputer program instructions may be stored by a memory 204 of thecomputing device 200 and executed by a processor 202 of the computingdevice 200. As will be appreciated, any such computer programinstructions may be loaded onto a computer or other programmableapparatus (e.g., hardware) to produce a machine, such that the resultingcomputer or other programmable apparatus implements the functionsspecified in the flowchart blocks. These computer program instructionsmay also be stored in a computer-readable memory that may direct acomputer or other programmable apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture, the execution of whichimplements the functions specified in the flowchart blocks. The computerprogram instructions may also be loaded onto a computer or otherprogrammable apparatus to cause a series of operations to be performedon the computer or other programmable apparatus to produce acomputer-implemented process such that the instructions executed on thecomputer or other programmable apparatus provide operations forimplementing the functions specified in the flowchart blocks.

The flowchart blocks support combinations of means for performing thespecified functions and combinations of operations for performing thespecified functions. It will be understood that one or more blocks ofthe flowcharts, and combinations of blocks in the flowcharts, can beimplemented by special purpose hardware-based computer systems whichperform the specified functions, or combinations of special purposehardware with computer instructions.

What is claimed is:
 1. A method for safety compliance determinations,the method comprising: receiving three-dimensional (3D) image data, the3D image data indicative of a field of view of a 3D imager that includesa first user upon which to perform a compliance determination;determining a fit parameter associated with a safety device of the firstuser within the field of view of the 3D imager based upon the 3D imagedata, wherein the fit parameter is indicative of an associatedpositioning of the safety device relative to the first user; comparingthe fit parameter with a compliance threshold associated with the safetydevice; generating an alert signal in an instance in which the fitparameter fails to satisfy the compliance threshold; and modifying thecompliance threshold associated with the safety device based upon one ormore iterative determinations of the fit parameter associated with thesafety device.
 2. The method according to claim 1, wherein generatingthe alert signal further comprises generating an adjustment notificationcomprising a modification of the positioning of the safety devicerelative to the first user.
 3. The method according to claim 1, whereingenerating the alert signal further comprises preventing access for thefirst user to one or more systems.
 4. The method according to claim 1,wherein determining the fit parameter further comprises supplying the 3Dimage data to an artificial neural network.
 5. The method according toclaim 1, wherein the 3D image data comprises an N-dimensional matrixcontaining one or more values indicative of coordinates of verticesforming polygons within the field of view of the 3D imager including thefirst user.
 6. The method according to claim 5, wherein determining thefit parameter further comprises: reducing the N-dimensional matrix intoa one-dimensional (1D) array; and determining the fit parameter basedupon a comparison between each value of the 1D array and one or morevalues associated with 3D image data indicative of the field of view ofthe 3D imager that includes a second user.
 7. An apparatus for safetycompliance determinations comprising: a three-dimensional (3D) imagerconfigured to generate 3D image data indicative of a field of view ofthe 3D imager that includes a first user upon which to perform acompliance determination; and a computer device configured to: generatea fit parameter associated with a safety device of the first user withinthe field of view of the 3D imager based upon the 3D image data, whereinthe fit parameter is indicative of an associated positioning of thesafety device relative to the first user; compare the fit parameter witha compliance threshold associated with the safety device; generate analert signal in an instance in which the fit parameter fails to satisfythe compliance threshold; and modify the compliance threshold associatedwith the safety device based upon one or more iterative determinationsof the fit parameter associated with the safety device.
 8. The apparatusaccording to claim 7, wherein the alert signal further comprises anadjustment notification comprising a modification of the positioning ofthe safety device relative to the first user.
 9. The apparatus accordingto claim 7, wherein the alert signal further comprises instructions toprevent access for the first user to one or more systems.
 10. Theapparatus according to claim 7, wherein the computing device is furtherconfigured to determine the fit parameter by supplying the 3D image datato an artificial neural network.
 11. The apparatus according to claim 7,wherein the 3D image data comprises an N-dimensional matrix containingone or more values indicative of coordinates of vertices formingpolygons within the field of view of the 3D imager including the firstuser.
 12. The apparatus according to claim 11, wherein the computingdevice is further configured to: reduce the N-dimensional matrix into aone-dimensional (1D) array; and determine the fit parameter based upon acomparison between each value of the 1D array and one or more valuesassociated with 3D image data indicative of the field of view of the 3Dimager that includes a second user.
 13. A non-transitorycomputer-readable storage medium for using an apparatus for safetycompliance determinations, the non-transitory computer-readable storagemedium storing instructions that, when executed, cause the apparatus to:receive three-dimensional (3D) image data, the 3D image data indicativeof a field of view of a 3D imager that includes a first user upon whichto perform a compliance determination, generate a fit parameterassociated with a safety device of the first user within the field ofview of the 3D imager based upon the 3D image data, wherein the fitparameter is indicative of an associated positioning of the safetydevice relative to the first user, compare the fit parameter with acompliance threshold associated with the safety device; generate analert signal in an instance in which the fit parameter fails to satisfythe compliance threshold; and modify the compliance threshold associatedwith the safety device based upon one or more iterative determinationsof the fit parameter associated with the safety device.
 14. Thenon-transitory computer-readable storage medium according to claim 13,wherein the non-transitory computer-readable storage medium storesinstructions that, when executed, cause the apparatus to generate anadjustment notification comprising a modification of the positioning ofthe safety device relative to the first user.
 15. The non-transitorycomputer-readable storage medium according to claim 13, wherein thenon-transitory computer-readable storage medium stores instructionsthat, when executed, cause the apparatus to prevent access for the firstuser to one or more systems.
 16. The non-transitory computer-readablestorage medium according to claim 13, wherein the non-transitorycomputer-readable storage medium stores instructions that, whenexecuted, cause the apparatus to supply the 3D image data to anartificial neural network.
 17. The non-transitory computer-readablestorage medium according to claim 13, wherein the 3D image datacomprises an N-dimensional matrix containing one or more valuesindicative of coordinates of vertices forming polygons within the fieldof view of the 3D imager including the first user.
 18. Thenon-transitory computer-readable storage medium according to claim 17,wherein the non-transitory computer-readable storage medium storesinstructions that, when executed, cause the apparatus to: reduce theN-dimensional matrix into a one-dimensional (1D) array; and determinethe fit parameter based upon a comparison between each value of the 1Darray and one or more values associated with 3D image data indicative ofthe field of view of the 3D imager that includes a second user.